Gemini 4 Argon
Neural Network
Gemini 4 Argon is Google's flagship reasoning model for code, math, science, and multi-step agents.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 943 718 tokens
- Reasoning effort
- low, medium, high
- Capabilities
- reasoning, function calling, Batch API
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
Image prompt
(per 1K tokens)
How it works Gemini 4 Argon?
Gemini 4 Argon is Google's flagship reasoning model designed for complex engineering: architecture analysis, multi-file refactoring, debugging, and deep mathematical and scientific tasks. The model maintains long-range context, enabling autonomous multi-step scenarios where remembering previous steps is crucial. In BotHub, it is accessible from Russia without a VPN or foreign card, with payment via Russian cards in rubles based on usage. Over 250 models are available in one window, making it easy to compare answers. For production, there is a unified OpenAI-compatible API, so you won't need to rewrite integrations. Dialogues are encrypted via AES-GCM, and data is not saved. For companies, we offer contracts, invoices, electronic document management (EDO), and an admin panel with limits. Use Argon for reviewing and refactoring large codebases, finding floating bugs, mathematical and STEM derivations, research analytics with long chains of thought, and agent pipelines where the model plans and executes steps itself.Frequently asked questions about Gemini 4 Argon
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
gemini-4-argon is a Google text model with reasoning before answering, function calling, and image/document input. It is suitable for working with code, analyzing voluminous documentation, STEM tasks, and agent scenarios requiring structured JSON output. Context window: 1,048,576 tokens.
In a single response, the model outputs up to 65,536 tokens—dozens of pages: a large technical document, a detailed analysis, or a voluminous code file. If the task is larger, break it into parts: the 1,048,576-token window allows keeping the entire conversation in context.
Yes, the reasoning mode is confirmed in the model's specifications. Before the final answer, it processes the task step-by-step—this is noticeable in math, logic, code debugging, and multi-step instructions. For simple questions, the difference is small; for complex ones, it significantly increases the chance of getting a correct result.
Yes, both. You describe the available tools, the model decides which one to call and with what arguments, and returns the result as structured JSON. Through BotHub's unified OpenAI-compatible API, this connects to your agents and pipelines without rewriting the integration.
Images and documents—yes: send a screenshot, diagram, PDF, or table and ask to analyze the content. Audio and video support is not noted in our data, so for such files, it is more convenient to switch to a specialized model in the same BotHub window.
There are no separate measurements by language in our data, so we will not provide exact figures. It is easier to check with your own task: open a chat on the website or in the BotHub Telegram bot, run a couple of typical queries, and if you wish, compare the answer with another model in the same window.